CG and CH seq preferences (no coverage filtering)
Izaskun Mallona
Per Baubec request (October 17th 2019). Largely based upon cph_quickchanges_simpler.Rmd; older report available at taupo
Requires extract_motifs_frequency_from_bam_binary.sh to be run first, as wrapped up by (no coverage filter) motif_extract_run_nov_2019_no_coverage_filtering.sh.
Motifs are classified as methylated or unmethylated and counted, without aggregating average methylation values per sample to avoid batch/genotype effects.
Config
Data genotypes/annotations
Please check/update annotations:
DT::datatable(as.data.frame(annot),
extensions = c("Buttons", "FixedColumns"),
rownames = FALSE,
options = list(dom = "Bfrtip",
scrollX = TRUE,
fixedColumns = list(leftColumns = 1),
buttons = c("csv", "excel")))Note that the merged datasets artificially gather samples from different runs, as follows:
DT::datatable(as.data.frame(tmp),
extensions = c("Buttons", "FixedColumns"),
rownames = FALSE,
options = list(dom = "Bfrtip",
scrollX = TRUE,
fixedColumns = list(leftColumns = 1),
buttons = c("csv", "excel")))Overview
Aim
- Hypothesis: DNMTs (DNA meth transferases) show sequence specificity
- Test: check whether methylated/unmethylated motifs representation
Design
knitr::include_graphics("/home/imallona/src/cg_context/stranded_dnameth.png")
- Flow
- Call DNA methylation for every CpG in a strand-specific manner
- Collapse counts (methylated/unmethylated) for each context 6-mer
NNNCGNNN - Get nucleotide proportions for each position (i.e. 1 to 8) for methylated and unmethylated motifs separately
- Get a log2 ratio
Datasets load
sources <- grep('cg_context|neuro', list.files(WD, recursive = TRUE, pattern = 'motif_counts_*'),
invert = TRUE, value = TRUE)Removing the merged 3a1, that only contains a single run
sources <- grep('d3a1_merged', sources, value = TRUE, invert = TRUE)## dictionary of the whole set of motifs
nucl <- c('T', 'C', 'G', 'A')
mdict <- list('cg' = NULL, 'ch' = NULL)
mdict$cg <- apply(expand.grid('1' = nucl, '2' = nucl, '3' = nucl,
'4' = 'C', '5' = 'G',
'6' = nucl, '7' = nucl, '8' = nucl ),
1,
function(x) paste(x, collapse = ''))
mdict$ch <- apply(expand.grid('1' = nucl, '2' = nucl, '3' = nucl,
'4' = 'C', '5' = c('A', 'T', 'C'),
'6' = nucl, '7' = nucl, '8' = nucl),
1,
function(x) paste(x, collapse = ''))
for (item in names(mdict)) {
fd <- mdict[[item]]
fd <- sort(as.character(fd))
fd <- data.frame(motif = fd, count = 0)
rownames(fd) <- fd$motif
mdict[[item]] <- fd
rm(fd)
}
## filling with zeroes the motifs that are not present in some datasets
for (context in c('cg', 'ch')) {
for (genotype in dirname(sources)) {
for (state in c('meth', 'unmeth')) {
curr <- d[[context]][[genotype]][[state]]
mis <- setdiff(rownames(mdict[[context]]), rownames(curr))
curr <- rbind(curr, mdict[[context]][mis,])
## lexicographical sorting
curr <- curr[order(as.character(curr$motif)),]
d[[context]][[genotype]][[state]] <- curr
stopifnot(nrow(curr) == nrow(mdict[[context]]))
rm(curr, mis)
}
}
}Saving the data as an R object with the following structure:
- context (cg or ch)
- sample (i.e. qko)
- status (meth or unmeth)
- dataframe the columns motif, number of instances
save(x = d, file = file.path(WD, 'counts_nested_list.RData'))
## str(d)Seqlogo-based analysis
Based in nucleotide deviations from the 0.25:0.25:0.25:0.25 expectations for each nucleotide for each position.
proportion <- function(x){
rs <- sum(x);
return(x / rs);
}
deviation_from_proportion <- function(x){
rs <- sum(x);
## return((x / rs) - 0.25)
return((x / rs) / 0.25)
}
log2ratio_proportion <- function(x){
rs <- sum(x);
## return((x / rs) - 0.25)
return(log2((x / rs) - 0.25))
}Data processing
Nucleotide weights are computed as if a PWM. We first get the nucleotide representation in methylated xor unmethylated motifs, and them compute the log2 enrichment of the meth/unmeth ratio to get a positive weight for overrepresentations. A step by step analysis is provided below (header Processing steps in detail).
outer <- list()
for (context in names(d)) {
melted <- list()
for (genotype in names(d[[context]])) {
## print(genotype)
selected <- d[[context]][[genotype]]$meth
for_pwm <- data.frame(A = rep(NA, nchar(selected$motif[1])),
C = NA,
G = NA,
T = NA)
for (i in 1:nchar(selected$motif[1])) {
id <- paste0('d',i)
selected[, id] <- substr(selected$motif, i, i)
for (nt in c('A', 'T', 'C', 'G')) {
tsum <- 0
tsum <- sum(selected[selected[,id] == nt,'count'])
for_pwm[i, nt] <- tsum
}
}
pwm <- makePWM(apply(for_pwm, 1, proportion))
pwm_meth <- pwm
## same for unmeth
selected <- d[[context]][[genotype]]$unmeth
for_pwm <- data.frame(A = rep(NA, nchar(selected$motif[1])),
C = NA,
G = NA,
T = NA)
for (i in 1:nchar(selected$motif[1])) {
id <- paste0('d',i)
selected[, id] <- substr(selected$motif, i, i)
for (nt in c('A', 'T', 'C', 'G')) {
tsum <- 0
tsum <- sum(selected[selected[,id] == nt,'count'])
for_pwm[i, nt] <- tsum
}
}
pwm <- makePWM(apply(for_pwm, 1, proportion))
pwm_unmeth <- pwm
deviations <- log2(pwm_meth@pwm /pwm_unmeth@pwm)
data.m <- melt(deviations)
data.m$genotype <- genotype
melted[[genotype]] <- data.m
outer[[context]] <- melted
}
}Processing steps in detail
Let’s visualize the calculations for sample tko+d3a2_merged and a context CG
context <- 'cg' ; genotype <- 'tko+d3a2_merged'
selected <- d[[context]][[genotype]]$meth
for_pwm <- data.frame(A = rep(NA, nchar(selected$motif[1])),
C = NA, G = NA, T = NA)
for (i in 1:nchar(selected$motif[1])) {
id <- paste0('d',i)
selected[, id] <- substr(selected$motif, i, i)
for (nt in c('A', 'T', 'C', 'G')) {
tsum <- 0
tsum <- sum(selected[selected[,id] == nt,'count'])
for_pwm[i, nt] <- tsum
}
}
pwm <- makePWM(apply(for_pwm, 1, proportion))
cat(sprintf('First we calculate the nucleotide proportions for each nucleotide in methylated motifs\n'))## First we calculate the nucleotide proportions for each nucleotide in methylated motifsprint(kable(pwm@pwm))##
##
## 1 2 3 4 5 6 7 8
## --- ---------- ---------- ---------- --- --- ---------- ---------- ----------
## A 0.2653976 0.1846908 0.3127135 0 0 0.2078319 0.2464551 0.3020369
## C 0.2570343 0.3280534 0.2994549 1 0 0.2979749 0.2892987 0.2562254
## G 0.2350310 0.1723616 0.1820777 0 1 0.1975226 0.2248220 0.1793862
## T 0.2425372 0.3148942 0.2057539 0 0 0.2966707 0.2394243 0.2623514cat("\n")pwm_meth <- pwm
## same for unmeth
selected <- d[[context]][[genotype]]$unmeth
for_pwm <- data.frame(A = rep(NA, nchar(selected$motif[1])),
C = NA,
G = NA,
T = NA)
for (i in 1:nchar(selected$motif[1])) {
id <- paste0('d',i)
selected[, id] <- substr(selected$motif, i, i)
for (nt in c('A', 'T', 'C', 'G')) {
tsum <- 0
tsum <- sum(selected[selected[,id] == nt,'count'])
for_pwm[i, nt] <- tsum
}
}
pwm <- makePWM(apply(for_pwm, 1, proportion))
cat('We do the same for unmethylated motifs\n')## We do the same for unmethylated motifsprint(kable(pwm@pwm))##
##
## 1 2 3 4 5 6 7 8
## --- ---------- ---------- ---------- --- --- ---------- ---------- ----------
## A 0.2777496 0.2696658 0.2702918 0 0 0.2470913 0.2426310 0.2340711
## C 0.2483383 0.2674804 0.2567395 1 0 0.1964955 0.2063492 0.2191563
## G 0.2354485 0.2402713 0.2302734 0 1 0.2880217 0.3020977 0.2760242
## T 0.2384636 0.2225825 0.2426953 0 0 0.2683915 0.2489221 0.2707485cat("\n") pwm_unmeth <- pwm
cat(sprintf('We finally calculate the log2 (meth/unmeth) ratios\n'))## We finally calculate the log2 (meth/unmeth) ratiosdeviations <- log2(pwm_meth@pwm /pwm_unmeth@pwm)
print(kable(deviations)) ##
##
## 1 2 3 4 5 6 7 8
## --- ----------- ----------- ----------- ---- ---- ----------- ----------- -----------
## A -0.0656295 -0.5460601 0.2103238 NaN NaN -0.2496268 0.0225606 0.3677781
## C 0.0496542 0.2944975 0.2220335 0 NaN 0.6006940 0.4874724 0.2254537
## G -0.0025610 -0.4792261 -0.3387937 NaN 0 -0.5441600 -0.4262323 -0.6217255
## T 0.0244368 0.5005267 -0.2382263 NaN NaN 0.1445234 -0.0561250 -0.0454525Coloring by annotations
All experiments
for (context in names(outer)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
melted2 <- merge(melted2, annot, by = 'sample', all.x = TRUE)
print(ggplot(melted2, aes(position, deviation, fill=as.factor(sample)))+
geom_bar(position="dodge",stat="identity") +
scale_x_continuous(breaks = 1:8) +
facet_wrap(~id+nucleotide, ncol= 4) +
xlab("position") +
ylab('log2fc meth/unmeth') +
theme(legend.position = 'none') + ggtitle(context))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
Per sample quality checks
Coverage (total number of methylated plus unmethylated motifs detected).
counts <- list()
for (context in names(d)) {
tmp <- list()
for (sample in names(d[[context]])) {
meth <- d[[context]][[sample]]$meth
meth$sample <- sample
meth$context <- context
meth$status <- 'meth'
unmeth <- d[[context]][[sample]]$unmeth
unmeth$sample <- sample
unmeth$context <- context
unmeth$status <- 'unmeth'
tmp[[sample]] <- rbind(meth, unmeth)
}
counts[[context]] <- do.call(rbind.data.frame, tmp)
}for (context in names(counts)) {
sums <- aggregate(counts[[context]]$count,
by=list(counts[[context]]$sample,
counts[[context]]$status), sum)
colnames(sums) <- c('sample', 'status', 'count')
sums <- merge(sums, annot, by ='sample')
print(ggplot(sums,
aes(x = sample, y = count , fill = factor(id)))+
geom_bar(stat="identity",position="dodge") +
scale_y_continuous(trans='log10') +
facet_wrap(~status , nrow = 1) +
xlab("Sample") + ylab("Cum. number of motifs detected") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(context))
}
And by sequencing technology
for (context in names(counts)) {
sums <- aggregate(counts[[context]]$count,
by=list(counts[[context]]$sample,
counts[[context]]$status), sum)
colnames(sums) <- c('sample', 'status', 'count')
sums <- merge(sums, annot, by ='sample')
print(ggplot(sums,
aes(x = sample, y = count , fill = factor(id)))+
geom_bar(stat="identity",position="dodge") +
scale_y_continuous(trans='log10') +
facet_wrap(~status , nrow = 1) +
xlab("Sample") + ylab("Cum. number of motifs detected") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(context))
}
Total number of motifs detected at least once (methylated or unmethylated).
Note that for CG we expect 4^6=4096, and for CH 4^6*3=12288.
for (context in names(counts)) {
detects <- aggregate(counts[[context]]$count,
by=list(counts[[context]]$sample,
counts[[context]]$status), function(x) sum(x >=1))
colnames(detects) <- c('sample', 'status', 'count')
detects <- merge(detects, annot, by ='sample')
print(ggplot(detects,
aes(x = sample, y = count , fill = factor(id)))+
geom_bar(stat="identity",position="dodge") +
scale_y_continuous(trans='log10') +
facet_wrap(~status , nrow = 1) +
xlab("Sample") + ylab("Number of motifs detected (at least once)") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(context))
}
Per nucleotide and sample
for (context in names(d)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
print(ggplot(melted2, aes(position, deviation, fill=as.factor(sample)))+
geom_bar(position="dodge",stat="identity") +
scale_x_continuous(breaks = 1:8) +
facet_wrap(~nucleotide,nrow = 2) + xlab("position") +
ylab('log2fc meth/unmeth') +
ggtitle(context) +
guides(fill = guide_legend(nrow = length(unique(melted2$sample)))) +
theme(legend.position="bottom", legend.title=element_text(size=5),
legend.text=element_text(size=8), legend.key.size = unit(0.7, "line")))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
for (context in names(d)) {
idx <- names(sort(sapply(outer[[context]], function(x)
return(x[x$Var1 == 'C' & x$Var2 == 2, 'value']))))
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
print(ggplot(melted2, aes(position, deviation, fill=as.factor(sample)))+
geom_bar(position="dodge",stat="identity") +
scale_x_continuous(breaks = 1:8) +
facet_wrap(~nucleotide,nrow = 2) + xlab("position") +
ylab('log2fc meth/unmeth') +
ggtitle(context) +
guides(fill = guide_legend(nrow = length(unique(melted2$sample)))) +
theme(legend.position="bottom", legend.title=element_text(size=5),
legend.text=element_text(size=8), legend.key.size = unit(0.7, "line")))
}Per nucleotide, celltype and annotation
for (context in names(d)) {
tmp <- as.numeric(annot[names(outer$cg),'id_short'])
names(tmp) <- names(outer$cg)
## actually this is not being used
idx <- names(sort(tmp))
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
annot$sample <- rownames(annot)
melted2 <- merge(melted2, annot, by = 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
print(ggplot(melted2, aes(position, deviation, fill=as.factor(id_short)))+
geom_bar(position="dodge",stat="identity") +
scale_x_continuous(breaks = 1:8) +
facet_wrap(~celltype + nucleotide, nrow = 2) + xlab("position") +
ylab('log2fc meth/unmeth') +
ggtitle(context))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
Per celltype, annotation and nucleotide
for (context in names(d)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
annot$sample <- rownames(annot)
melted2 <- merge(melted2, annot, by = 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
print(ggplot(melted2, aes(nucleotide, deviation, fill=as.factor(position)))+
geom_bar(position="dodge",stat="identity") +
facet_wrap(~celltype + id_short, nrow = 2) + xlab("genotype") +
ylab('log2fc meth/unmeth') +
ggtitle(context))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
for (context in names(d)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
annot$sample <- rownames(annot)
melted2 <- merge(melted2, annot, by = 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
print(ggplot(melted2, aes(nucleotide, deviation, fill=as.factor(position)))+
geom_bar(position="dodge",stat="identity") +
facet_wrap(~celltype + id_short, nrow = 2) + xlab("id") +
ylab('log2fc meth/unmeth') +
ggtitle(context))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
Per sample and nucleotide
for (context in names(d)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
annot$sample <- rownames(annot)
melted2 <- merge(melted2, annot, by = 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
print(ggplot(melted2, aes(position, deviation, fill=as.factor(nucleotide)))+
geom_bar(position="dodge",stat="identity") +
scale_x_continuous(breaks = 1:8) +
facet_wrap(~sample, nrow=12) + xlab("position") + ylab('log2fc meth/unmeth') +
ggtitle(context))
}## Warning: Removed 228 rows containing missing values (geom_bar).
## Warning: Removed 152 rows containing missing values (geom_bar).
Naive Bayes
(ongoing)
Subset
Plotting some genotypes only
## Warning: Removed 66 rows containing missing values (geom_bar).
## Warning: Removed 66 rows containing missing values (geom_bar).
## Warning: Removed 44 rows containing missing values (geom_bar).
## Warning: Removed 44 rows containing missing values (geom_bar).
Clustering
Simple approach: cluster the overall rate of meth/unmeth per motif (this is biased by methylation level).
ratios <- sapply(d$cg, function(x) return(x$meth$count))/
(sapply(d$cg, function(x) return(x$unmeth$count)) + sapply(d$cg, function(x) return(x$meth$count)) )
rownames(ratios) <- d$cg[[1]]$unmeth$motif
ratios <- ratios[,c(targets, grep('*merged*', colnames(ratios), value = TRUE))]
hc <- hclust(dist(t(ratios)), method = 'ward.D')
plot(hc)
Annotated clustering
## hc2 <- hc
## hc2$labels <- sprintf('%s\n(%s)', hc$labels, annot[hc$labels,'id'])
dend <- as.dendrogram(hc)
## labels_colors(dend) <- as.numeric(as.factor( annot[hc$labels,'id']))
par(mar = c(4,10,1,22), xpd=TRUE)
plot(dend, horiz = TRUE)
colored_bars(cbind(as.numeric(as.factor(annot[hc$labels,c('id_short')])),
as.numeric(as.factor(annot[hc$labels,c('tech')])),
as.numeric(as.factor(annot[hc$labels,c('celltype')]))), dend, horiz = TRUE,
rowLabels = c('id', 'tech','celltype'))
legend("bottomleft",inset=c(-0.5,0), title ='id',
legend = levels(as.factor(annot[hc$labels,'id_short'])),
fill = 1:nlevels(as.factor(annot[hc$labels,'id'])))
legend("topleft", inset=c(-0.5,0), title = 'tech',
legend = levels(as.factor(annot[hc$labels,'tech'])),
fill = 1:nlevels(as.factor(annot[hc$labels,'tech'])))
rownames(annot) <- annot$sample
annot <- annot[,-1]
pheatmap(ratios, cluster.rows = TRUE, cluster.cols = TRUE, annotation_col = annot,
scale = 'none', show_rownames = FALSE)
## note the scaling and centering by column
pheatmap(ratios, cluster.rows = TRUE, cluster.cols = TRUE, annotation_col = annot,
scale = 'column', show_rownames = FALSE)
Heatmap representations
Per Tuncay request 11 Nov 2019
for (context in names(d)) {
melted2 <- do.call(rbind.data.frame, outer[[context]])
colnames(melted2) <- c('nucleotide', 'position', 'deviation', 'sample')
annot$sample <- rownames(annot)
melted2 <- merge(melted2, annot, by = 'sample')
melted2$sample <- factor(x = melted2$sample, levels = idx)
for (sample in levels(melted2$sample)) {
tmp <- melted2[melted2$sample == sample,]
wide <- dcast(tmp[,c('deviation', 'nucleotide', 'position')], nucleotide ~ position,
value.var = 'deviation')
rownames(wide) <- wide$nucleotide
wide <- wide[,-1]
## while (!is.null(dev.list())) dev.off()
## cat(sprintf('\n%s %s\n\n\n', context, sample))
cat('\n\n\n\n')
pheatmap(wide, cluster_rows = FALSE, cluster_cols = FALSE,
color = colorRampPalette(c('red', 'white', 'blue'))(60),
breaks = seq(-3, 3, 0.1), main = sprintf('%s\n%s', context, sample))
}
}
Timestamp
date()## [1] "Mon Nov 18 17:38:52 2019"sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/local/R/R-3.6.0/lib/libRblas.so
## LAPACK: /usr/local/R/R-3.6.0/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] dendextend_1.12.0 viridis_0.5.1 viridisLite_0.3.0
## [4] naivebayes_0.9.6 DT_0.9 gridExtra_2.3
## [7] ggseqlogo_0.1 ggfortify_0.4.7 ggplot2_3.2.1
## [10] seqLogo_1.50.0 pheatmap_1.0.12 reshape2_1.4.3
## [13] knitr_1.25
##
## loaded via a namespace (and not attached):
## [1] pkgload_1.0.2 tidyr_1.0.0 jsonlite_1.6
## [4] shiny_1.3.2 assertthat_0.2.1 highr_0.8
## [7] stats4_3.6.0 yaml_2.2.0 remotes_2.1.0
## [10] sessioninfo_1.1.1 pillar_1.4.2 backports_1.1.4
## [13] glue_1.3.1 digest_0.6.20 RColorBrewer_1.1-2
## [16] promises_1.0.1 colorspace_1.4-1 htmltools_0.3.6
## [19] httpuv_1.5.2 plyr_1.8.4 pkgconfig_2.0.2
## [22] devtools_2.2.0 purrr_0.3.2 xtable_1.8-4
## [25] scales_1.0.0 processx_3.4.1 later_0.8.0
## [28] tibble_2.1.3 usethis_1.5.1 ellipsis_0.2.0.1
## [31] withr_2.1.2 lazyeval_0.2.2 cli_1.1.0
## [34] magrittr_1.5 crayon_1.3.4 mime_0.7
## [37] memoise_1.1.0 evaluate_0.14 ps_1.3.0
## [40] fs_1.3.1 pkgbuild_1.0.5 tools_3.6.0
## [43] prettyunits_1.0.2 lifecycle_0.1.0 stringr_1.4.0
## [46] munsell_0.5.0 callr_3.3.1 compiler_3.6.0
## [49] rlang_0.4.0 htmlwidgets_1.3 crosstalk_1.0.0
## [52] labeling_0.3 rmarkdown_1.15 testthat_2.2.1
## [55] gtable_0.3.0 codetools_0.2-16 R6_2.4.0
## [58] dplyr_0.8.3 zeallot_0.1.0 rprojroot_1.3-2
## [61] desc_1.2.0 stringi_1.4.3 Rcpp_1.0.2
## [64] vctrs_0.2.0 png_0.1-7 tidyselect_0.2.5
## [67] xfun_0.9devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.6.0 (2019-04-26)
## os Ubuntu 16.04.6 LTS
## system x86_64, linux-gnu
## ui X11
## language en_CA:en
## collate en_CA.UTF-8
## ctype en_CA.UTF-8
## tz Europe/Zurich
## date 2019-11-18
##
## ─ Packages ──────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
## backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.0)
## callr 3.3.1 2019-07-18 [1] CRAN (R 3.6.0)
## cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.0)
## codetools 0.2-16 2018-12-24 [3] CRAN (R 3.6.0)
## colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
## crosstalk 1.0.0 2016-12-21 [1] CRAN (R 3.6.0)
## dendextend * 1.12.0 2019-05-11 [1] CRAN (R 3.6.0)
## desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
## devtools 2.2.0 2019-09-07 [1] CRAN (R 3.6.0)
## digest 0.6.20 2019-07-04 [1] CRAN (R 3.6.0)
## dplyr 0.8.3 2019-07-04 [1] CRAN (R 3.6.0)
## DT * 0.9 2019-09-17 [1] CRAN (R 3.6.0)
## ellipsis 0.2.0.1 2019-07-02 [1] CRAN (R 3.6.0)
## evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
## fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
## ggfortify * 0.4.7 2019-05-26 [2] CRAN (R 3.6.0)
## ggplot2 * 3.2.1 2019-08-10 [1] CRAN (R 3.6.0)
## ggseqlogo * 0.1 2017-07-25 [2] CRAN (R 3.6.0)
## glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
## gridExtra * 2.3 2017-09-09 [1] CRAN (R 3.6.0)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
## highr 0.8 2019-03-20 [1] CRAN (R 3.6.0)
## htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.6.0)
## htmlwidgets 1.3 2018-09-30 [1] CRAN (R 3.6.0)
## httpuv 1.5.2 2019-09-11 [1] CRAN (R 3.6.0)
## jsonlite 1.6 2018-12-07 [1] CRAN (R 3.6.0)
## knitr * 1.25 2019-09-18 [1] CRAN (R 3.6.0)
## labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
## later 0.8.0 2019-02-11 [1] CRAN (R 3.6.0)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.0)
## lifecycle 0.1.0 2019-08-01 [1] CRAN (R 3.6.0)
## magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.0)
## memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
## mime 0.7 2019-06-11 [1] CRAN (R 3.6.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.0)
## naivebayes * 0.9.6 2019-06-03 [2] CRAN (R 3.6.0)
## pheatmap * 1.0.12 2019-01-04 [1] CRAN (R 3.6.0)
## pillar 1.4.2 2019-06-29 [1] CRAN (R 3.6.0)
## pkgbuild 1.0.5 2019-08-26 [1] CRAN (R 3.6.0)
## pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.6.0)
## pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
## plyr 1.8.4 2016-06-08 [1] CRAN (R 3.6.0)
## png 0.1-7 2013-12-03 [1] CRAN (R 3.6.0)
## prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.6.0)
## processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.0)
## promises 1.0.1 2018-04-13 [1] CRAN (R 3.6.0)
## ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.0)
## purrr 0.3.2 2019-03-15 [1] CRAN (R 3.6.0)
## R6 2.4.0 2019-02-14 [1] CRAN (R 3.6.0)
## RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.6.0)
## Rcpp 1.0.2 2019-07-25 [1] CRAN (R 3.6.0)
## remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.0)
## reshape2 * 1.4.3 2017-12-11 [1] CRAN (R 3.6.0)
## rlang 0.4.0 2019-06-25 [1] CRAN (R 3.6.0)
## rmarkdown 1.15 2019-08-21 [1] CRAN (R 3.6.0)
## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
## scales 1.0.0 2018-08-09 [1] CRAN (R 3.6.0)
## seqLogo * 1.50.0 2019-05-02 [2] Bioconductor
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
## shiny 1.3.2 2019-04-22 [1] CRAN (R 3.6.0)
## stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
## stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
## testthat 2.2.1 2019-07-25 [1] CRAN (R 3.6.0)
## tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
## tidyr 1.0.0 2019-09-11 [1] CRAN (R 3.6.0)
## tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.0)
## usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
## vctrs 0.2.0 2019-07-05 [1] CRAN (R 3.6.0)
## viridis * 0.5.1 2018-03-29 [1] CRAN (R 3.6.0)
## viridisLite * 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
## withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
## xfun 0.9 2019-08-21 [1] CRAN (R 3.6.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 3.6.0)
## yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
## zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.0)
##
## [1] /home/Shared/Rlib/release-3.9-lib
## [2] /home/imallona/R/x86_64-pc-linux-gnu-library/3.6
## [3] /usr/local/R/R-3.6.0/library